It's a project that claims to use cryptocurrency to distribute money across the world, though its bigger ambition is to create a global identity system called "World ID" that relies on individuals' unique biometric data to prove that they are humans. It officially launched on July 24 in more than 20 countries, and Sam Altman, the CEO of OpenAI and one of the biggest tech celebrities right now, is one of the cofounders of the project. The company makes big, idealistic promises: that it can deliver a form of universal basic income through technology to make the world a better and more equitable place, while offering a way to verify your humanity in a digital future filled with nonhuman intelligence, which it calls "proof of personhood." If you're thinking this sounds like a potential privacy nightmare, you're not alone. Luckily, we have someone I'd consider the Worldcoin expert on staff here at MIT Technology Review.

On a sunny morning last December, Iyus Ruswandi, a 35-year-old furniture maker in the village of Gunungguruh, Indonesia, was woken up early by his mother. A technology company was holding some kind of "social assistance giveaway" at the local Islamic elementary school, she said, and she urged him to go. When he got there, representatives of Worldcoin were collecting emails and phone numbers, or aiming a futuristic metal orb at villagers' faces to scan their irises and other biometric data. Two months before Worldcoin appeared in Ruswandi's village, the San Francisco–based company called Tools for Humanity emerged from stealth mode. The company's website described Worldcoin as an Ethereum-based "new, collectively owned global currency that will be distributed fairly to as many people as possible."

Meta learning approaches to few-shot classification are computationally efficient at test time requiring just a few optimization steps or single forward pass to learn a new task, but they remain highly memory-intensive to train. This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken. Harnessing the performance gains offered by large images thus requires either parallelizing the meta-learner across multiple GPUs, which may not be available, or trade-offs between task and image size when memory constraints apply. We improve on both options by proposing LITE, a general and memory efficient episodic training scheme that enables meta-training on large tasks composed of large images on a single GPU. We achieve this by observing that the gradients for a task can be decomposed into a sum of gradients over the task's training images. This enables us to perform a forward pass on a task's entire training set but realize significant memory savings by back-propagating only a random subset of these images which we show is an unbiased approximation of the full gradient. We use LITE to train meta-learners and demonstrate new state-of-the-art accuracy on the real-world ORBIT benchmark and 3 of the 4 parts of the challenging VTAB MD benchmark relative to leading meta-learners. LITE also enables meta-learners to be competitive with transfer learning approaches but at a fraction of the test-time computational cost, thus serving as a counterpoint to the recent narrative that transfer learning is all you need for few-shot classification.

Leverage on the capabilities of AI, to maximize on the benefits of a great user experience. If you can do a better job than your competition at satisfying your customers, you are doing it right. By identifying the needs first and then drafting a'how to' plan to meet those needs. Two, artificial intelligence enhances that experience by morphing app behavior in line with the end-user expectation. So if you want addicted users, work on an AI empowered UX, geared on the right insights, and you are good to go.

Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.

Waymo, a self-driving car company spun out from Google's parent company Alphabet, has stopped developing features that require drivers to take control in dangerous situations, its chief executive has said. Autopilot reliance has left users prone to distractions and ill-prepared to manoeuvre, the company said. The decision followed experiments of the technology in Silicon Valley that showed test users napping, putting on makeup and fiddling with their phones as the vehicles travelled up to 56mph (90kph). Alphabet's self-driving car unit Waymo (pictured) has stopped developing features that required drivers to take control in dangerous situations, its chief executive said on Monday Other self-driving automakers include similar autopilot features for highway-driving in vehicles, but they require drivers to take over the steering wheel in tricky situations. Waymo planned to do the same.

Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, named collaborative ensemble learning. It uses probabilistic SVMs to model each user's profile (as CBF does).At the prediction phase, it combines a society OF users profiles, represented by their respective SVM models, to predict an active users preferences(the CF idea).The combination scheme is embedded in a probabilistic framework and retains an intuitive explanation.Moreover, collaborative ensemble learning does not require a global training stage and thus can incrementally incorporate new data.We report results based on two data sets. For the Reuters-21578 text data set, we simulate user ratings under the assumption that each user is interested in only one category. In the second experiment, we use users' opinions on a set of 642 art images that were collected through a web-based survey. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy.

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

It has previously been shown that a recommender based on immune system idiotypic principles can out perform one based on correlation alone. This paper reports the results of work in progress, where we undertake some investigations into the nature of this beneficial effect. The initial findings are that the immune system recommender tends to produce different neighbourhoods, and that the superior performance of this recommender is due partly to the different neighbourhoods, and partly to the way that the idiotypic effect is used to weight each neighbours recommendations.